Response Surface Methodology For Optimization Research Articles

Multi-objective optimization and performance assessment of response surface methodology (RSM), artificial neural network (ANN) and adaptive neuro-fuzzy interfence system (ANFIS) for estimation of fouling in phosphoric acid/steam heat exchanger

Fouling is a common occurrence in industrial heat exchangers, leading to a decrease of the thermal efficiency. This research addresses the challenge of applying various machine learning algorithms including Response Surface Methodology (RSM), Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) to effectively model the fouling resistance in a heat exchanger in phosphoric acid concentration loop. Subsequently, a multi-objective optimization approach was employed to minimize the fouling resistance. Confirmatory experiments are then conducted in the phosphoric acid concentration plant using optimized variables.The findings of this study indicate that the three models accurately align with one year of operational data, achieving a remarkably high coefficient of correlation (R). Among the models utilized, RSM demonstrates the highest level of prediction accuracy, with an R of 0.9998, accompanied by the lowest mean square error (MSE) of 5.9388 10−13 and root mean squared error (RMSE) of 7.7064 10−7. The RSM optimization process identifies the optimal conditions for variables such as time, acid inlet and outlet temperature, steam temperature, acid density, and volume flow rate, which are determined to be 114.805 h; 72.214 °C; 80.407 °C; 116.784 °C; 1642.47 kg/m3; and 2308.1 m3/h, respectively. The predicted fouling resistance demonstrates a strong correlation with the actual data, with a negligible percentage difference of 2.19 %, further validating the accuracy and reliability of the RSM model.

Development of partitioned rectangular-shaped heat sink for photovoltaic thermal systems and performance evaluation using TiO2/Water-based nanofluids

The current study presents a partitioned rectangular-shaped heat sink for improving the PVT system performance. The experiment was examined under various experimental parameters. Five cooler block types with different dimensions were arranged to share the same total contact area with the panel back surface. These cooler blocks were placed in different arrangements, namely (PVT-I, PVT-II, PVT-III, PVT-IV, PVT-V), and studied comparatively using water. The dimensions and numbers of each cooler block arrangement were altered, while maintaining the same surface area in contact with the photovoltaic panel. Through the response surface methodology (RSM) optimization, the optimal arrangement with the best performance was determined, and then TiO2/nanofluid was applied to that arrangement with different volumetric concentrations. Using water, the highest electrical and thermal efficiencies were obtained by case PVT-III with values of 19.39 % and 63.72 %, respectively. This was followed by PVT-II, PVT-V, PVT-I, and PVT-IV in that order for electrical efficiency; and at the same time by PVT-II, PVT-I, PVT-IV, and PVT-V in that order for thermal efficiency. The RSM method recommended the optimal arrangement case as case PVT-III. By using nanofluid in that case arrangement, the highest electrical efficiency value was recorded upon using 0.3 wt% and operating the system at 1.5 L/m and 900 W/m2. Regarding the thermal efficiency results, in that case, the highest and lowest values were recorded upon using 0.3 wt% and 0.1 wt%, respectively, with values of 72.49 % and 31.19 %. Increasing the volumetric concentrations positively reflected on the system's performance, resulting in high efficiencies being achieved. In addition, the highest electrical efficiencies were obtained at high levels of solar irradiance and flow rates.

Maximizing enhanced oil recovery via oxidative cracking of crude oil: employing air injection and H2O2 with response surface methodology optimization

The utilization of air injection as a method to enhance oil recovery in oil fields has gained prominence due to its cost-effectiveness and widespread availability, particularly in heavy oil production. This study focuses on optimizing the oxidative cracking process of Algerian crude oil by employing air injection supplemented with H2O2 and analyzing the interaction of key operating parameters like temperature and catalyst amount using response surface methodology. The predicted values derived from the response functions closely aligned with experimental data, demonstrating high accuracy (R2 = 0.9727 for liquid oil, R2 = 0.9176 for residue, and R2 = 0.7399 for gas phases). Using the developed second-order model, optimal conditions were determined through contour and surface plots, as well as regression equation analysis using Design software. At these optimal parameters (14.78 wt% of H2O2, 2 l min−1 of air flow, 100 ml of crude oil at 354.05 °C for 40 min), the oxidative cracking process yielded 96.32% liquid oil, 3.018% residue, and 0.662% gas products. Notably, the experimental produced liquid oil constituted 96.07 vol. %, matching well with the optimization outcomes. Physicochemical analysis of liquid product phase obtained from oxidative cracking process of petroleum confirmed the prevalence of light aliphatic compounds (C2-C11) at 70.59%, alongside 29.41% of C12-C36. The process also resulted in reduced viscosity, density, refractive index, and sulfur content in the liquid phase. The combination of air injection and H2O2 showcases promise in recovering residual oil effectively and contributes to the ongoing advancements in EOR techniques.

Response Surface Methodology for Optimization of Ultrasound-Assisted Antioxidants Extraction from Blackberry, Chokeberry and Raspberry Pomaces.

An investigation of the ultrasound-assisted extraction (UAE) of polyphenol-rich aqueous extracts from blackberry, chokeberry and raspberry pomaces was carried out. The aim of the study was to choose optimal conditions for UAE in order to obtain extracts rich in phenolic compounds. The optimization was carried out based on response surface methodology. The variable conditions were amplitude of ultrasound wave and extraction time, whereas responses were total polyphenol content and antioxidant capacity. Based on the ANOVA analysis, mathematical models were fitted and verified. The most effective conditions of amplitude and time were 98% and 5.00 min, 78% and 10.32 min and 90% and 11.56 min for blackberry pomace, chokeberry pomace and raspberry pomace, respectively. The actual results obtained in optimized conditions were comparable to the results predicted by the models. Additionally, the anthocyanin content in extracts was determined in the high-performance liquid chromatography assay. It was proven that response surface methodology could be a useful tool in the optimization of UAE processes for obtaining polyphenol-rich extracts from berry fruit pomaces.

Optimizing methanol synthesis from CO2 using graphene-based heterogeneous photocatalyst under RSM and ANN-driven parametric optimization for achieving better suitability.

Assessment of the performance of linear and nonlinear regression-based methods for estimating in situ catalytic CO2 transformations employing TiO2/Cu coupled with hydrogen exfoliation graphene (HEG) has been investigated. The yield of methanol was thoroughly optimized and predicted using response surface methodology (RSM) and artificial neural network (ANN) model after rigorous experimentation and comparison. Amongst the different types of HEG loading from 10 to 40 wt%, the 30 wt% in the HEG-TiO2/Cu assisted photosynthetic catalyst was found to be successful in providing the highest conversion efficiency of methanol from CO2. The most influencing parameters, HEG dosing and inflow rate of CO2, were found to affect the conversion rate in the acidic reaction regime (at pH of 3). According to RSM and ANN, the optimum methanol yields were 36.3 mg g-1 of catalyst and 37.3 mg g-1 of catalyst, respectively. Through the comparison of performances using the least squared error analysis, the nonlinear regression-based ANN showed a better determination coefficient (overall R2 > 0.985) than the linear regression-based RSM model (overall R2 ∼ 0.97). Even though both models performed well, ANN, consisting of 9 neurons in the input and 1 hidden layer, could predict optimum results closer to RSM in terms of agreement with the experimental outcome.

Removal of tartrazine and indigo carmine with layered double hydroxide-modified graphite nanocomposite: Isotherm, kinetics and response surface methodology (RSM) optimization

This research in focus investigates the removal of tartrazine (TZ) and indigo carmine (IC) dyes utilizing an AlCuFe-LDH/C nanocomposite as an adsorbent. The composite underwent comprehensive characterization through FE-SEM, EDX, FT-IR, XRD, and BET analyses. To gauge the efficacy of TZ and IC removal with the powdered adsorbent in use, the nanocomposites performance in surface adsorption of dyes species optimized, designed, and tested. Various parameters influencing the dyes adsorption under optimal conditions were considered, with a pH of 6.0 and an adsorption process time of 13 min selected. The concentrations of IC and TZ solutions were set at 11.5 mg/L, while a mass of 0.0143 g of adsorbent was applied. Under these conditions, the adsorbent exhibited impressive capabilities, removing 99.20 % of TZ and 88.5 % of IC. Equilibrium data were fitted to Langmuir, Freundlich, Temkin, and Dubinin-Radushkevich models, with the Langmuir isotherm model found to be the most fitting. Based on the Langmuir isotherm model, the adsorption capacities were determined to be 26.05 mg g−1 for TZ, 26.56 mg g−1 for IC. Additionally, the kinetic evaluation of experiments conducted at various time intervals revealed that the adsorption processes aligned well with second-order kinetic models. Regeneration studies indicated that the adsorbent could successfully retain dyes even after four cycles, with the adsorption percentage of AlCuFe-LDH/C for dyes remaining at 80 % after four adsorption–desorption cycles.

Multivariate modelling of AA6082-T6 drilling performance using RSM, ANN and response optimization

The AA6082-T6 was experimentally studied in the present research with respect to the drilling performance. Drill diameter, cutting speed and feed rate were examined, using a full factorial design. Mathematical modelling of the process was carried out using the Response Surface Methodology (RSM) as well as the Artificial Neural Network (ANN) techniques. The output results in terms of cutting force, torque and surface roughness, revealed high levels of correlation between the experimental and the predicted data. Specifically, the Mean Absolute Percentage Error (MAPE) values using RSM compared to the ones of the experiments, were equal to 2.14%, 3.49% and 6.16% for Fz, Mz and Ra respectively. The equivalent MAPE between the ANN and the experiments were found to be 2.19%, 1.82% and 2.85% accordingly. Moreover, the most significant terms were revealed, being the interaction D × f for the thrust force and the torque with contribution percentages equal to approximately 44% and 42% respectively, and the term D2 for the surface roughness with 51%. The evaluation of the machining parameters, identified their significance, enabling the selection of the optimal cutting parameters, which were obtained by the desirability function, taking into account the importance of the generated surface quality and the reduction of cost. The solutions given by this approach, pointed out the Ø9 tool, coupled with Vc = 50 m/min and f = 0.15mm/rev as a well-balanced combination, whereas the Ø9.9 tool used under the same conditions, yielded the best possible surface quality (appr. 0.2 μm).

Optimizing recombinant production of L-asparaginase 1 from Saccharomyces cerevisiae using response surface methodology.

L-asparaginase is an essential enzyme used in cancer treatment, but its production faces challenges like low yield, high cost, and immunogenicity. Recombinant production is a promising method to overcome these limitations. In this study, response surface methodology (RSM) was used to optimize the production of L-asparaginase 1 from Saccharomyces cerevisiae in Escherichia coli K-12 BW25113. The Box-Behnken design (BBD) was utilized for the RSM modeling, and a total of 29 experiments were conducted. These experiments aimed to examine the impact of different factors, including the concentration of isopropyl-b-LD-thiogalactopyranoside (IPTG), the cell density prior to induction, the duration of induction, and the temperature, on the expression level of L-asparaginase 1. The results revealed that while the post-induction temperature, cell density at induction time, and post-induction time all had a significant influence on the response, the post-induction time exhibited the greatest effect. The optimized conditions (induction at cell density 0.8 with 0.7mM IPTG for 4h at 30°C) resulted in a significant amount of L-asparaginase with a titer of 93.52μg/mL, which was consistent with the model-based prediction. The study concluded that RSM optimization effectively increased the production of L-asparaginase 1 in E. coli, which could have the potential for large-scale fermentation. Further research can explore using other host cells, optimizing the fermentation process, and examining the effect of other variables to increase production.

Optimization of VAWT installation with spatial and temporal complexity considerations

The constant need for fuel to meet the commercial sector’s ever-increasing demand has driven researchers to discover and optimize renewable energy resources, paving the way for sustainable production of reliable and clean energy resources. The goal of the current work is to close the gap in process parameter optimization needed to convert wind energy wake from traffic on highways into electrical energy utilizing vertical-axis wind turbines (VAWTs). The energy output from the VAWT is analyzed to investigate how it is impacted by the variations in multiple parameter settings. Using the central composite design (CCD), a three-level four-factor array was used to investigate the following parameters: VAWT vertical distance (VD) and horizontal distance (HD) as continuous parameters, while road side (S) and location (L) of VAWT as categorical parameters. To find the most important parameter, response surface methodology (RSM) optimization and an analysis of variance (ANOVA) test are performed. L accounts for 66.67% of the total variable, with S coming in second with 51.80%. Using the best results from RSM and ANOVA, a confirmation test is run, and the results show yields of 88.75% ± 0.05% and 87.5% ± 0.05%, respectively. Therefore, RSM and ANOVA can be utilized equally for optimization at the same VAWT design. Lastly, the findings of the economic and environmental evaluation demonstrate that, in comparison to the basic settings, VAWT operating at optimal settings can save up to 180% and 200% more energy and reduce carbon emissions, respectively.

Application of response surface methodology for optimization of removal sunset yellow from aqueous by Granular Ferric Hydroxide (GFH)

Color is the first pollutant that is detected in wastewater and can be discharged into the wastewater through various industries. Sunset yellow is an azo dye that is used in the food, health and pharmaceutical industries. Due to its low price and good quality, this color is used in industries more than natural colorants. The aim of this study was to investigate the effect of granular ferric hydroxide (GFH) on the removal of sunset yellow color from aqueous solution using the response surface methodology (RSM). In this study, GFH was employed to remove sunset yellow dye from the aqueous solution. Due to saving time, cost and getting to the optimum conditions, the effect of important factors on adsorption was optimized by the RSM. The morphology of the adsorbent was determined by scanning electron microscopy. The results revealed that optimum conditions for sunset yellow removal by GFH with design experiment were dye concentration= 24 mg/L, GFH dose= 2.4 g/L, reaction time= 25 min. In addition, based on the results, the best-fitted model for this study was the quadratic model (R2 =0.966, R2adj=0.79 and R2pred=0.936). According to the obtained results, GFH was successful in sunset yellow removal. Therefore, it is suggested to use this process to remove sunset yellow from aqueous solutions.

Investigation of chemically treated jute/kenaf/glass fiber with TiO2 nano-filler for tensile and impact characteristics

The essential objective of this work was to investigate the effect of silane treatment on the tensile and impact strength of composites made of jute/kenaf/glass fibers with the TiO2 nano-filler. This study also sought to optimize the factors connected with this treatment method. To develop and evaluate trials, the researchers used Response Surface Methodology (RSM) with central composite designs, emphasizing the optimization of silane concentration, immersion time, and nano-filler content. The researchers were able to effectively create quadratic models to forecast the tensile and impact strengths of the nano-composite throughout the silane treatment procedure. The adjustment of silane concentration, immersion duration, and nano-filler content discovered the best conditions for obtaining maximum tensile strength. The optimal values for silane concentration and immersion time have been identified to be 15 wt. % and 30 min, respectively. Considering these conditions, the composite’s tensile strength increased by 32.13% and its impact strength improved by 8.34% above the lowest values achieved from RSM optimization. The results demonstrate the practical value of silane treatment, notably in boosting tensile and impact strengths while enhancing interfacial adhesive among natural fibers and polymer matrices.

Optimized extraction of polyphenols with antioxidant and anti-biofilm activities and LC-MS/MS-based characterization of phlorotannins from Sargassum muticum

The Sargassum genus is rich in glycoglycerolipids, phytosterols, terpenoids, polyphenols, nitrogenous compounds, and other components, and it exhibits various biological activities. Response surface methodology optimization based on Box-Behnken design was applied to extract polyphenols with antioxidant and anti-biofilm activities from Sargassum muticum. Results indicated that the optimal extraction conditions were an ethanol concentration of 56%, a temperature of 58 °C, a time of 5 h, and a liquid/material ratio of 40 mL/g. Under these conditions, the contents of polyphenols and phlorotannins were 10.76 ± 0.42 mg/g and 1.62 ± 0.04 mg/g, respectively. The EtOH extract of S. muticum was further purified with organic solvents of different polarities, and the ethyl acetate fraction (HA-PTs) exhibited the highest purity of polyphenols and phlorotannins, along with the best antioxidant and anti-biofilm activities. In phytochemical analysis, the main substances identified were polysaccharides, phytosterols, polyphenols, flavonoids, and phlorotannins. According to UHPLC-DAD-ESI-MS/MS analysis, four types of phlorotannins were identified from HA-PTs: dibenzodioxine-1,3,6,8-tetraol, tetraphlorethol, pentaphlorethol, and fucopentaphlorethol. These results indicate that the polyphenols of S. muticum have the potential to become new natural antioxidants and antibacterial agents.

Experimental investigation, performance analysis, and optimization of hot air convective drying of date fruits via response surface methodology

A laboratory-scale hot air convective dryer (HACD) was used to dry date fruits surplus at 0.5 ± 0.02 kg kg−1 to a safe moisture level of 0.35 kg kg−1 under drying temperatures (T) of 50–70 °C with airflow rates (Q) of 0.18 and 0.28 m3 h−1. Kinetics, modeling, energy, economic feasibility, and color difference of the date fruits were analyzed and then optimized using response surface methodology (RSM) with two drying factors and eight responses. The drying time to reach the desired moisture level ranged from 9 to 28 h under different drying conditions. The proposed mathematical model expressed the moisture behavior over drying time with the highest R2 = 0.9998 and 0.9999, respectively, for 0.18 and 0.28 m3 h−1. Effective moisture diffusivity (Deff) was calculated using the starting accessibility parameter and varied between 9.9 × 10−8 and 3.3 × 10−7 m2 s−1. The energy analysis revealed that the specific energy consumption (SEC) varies between 22.04 and 43.05 kWh.kg−1 and the drying efficiency (η) are 16.91, 16.14, and 25.38% for 50, 60, and 70 °C, respectively. The RSM optimization using a multi-level design with two centurium points denoted the optimum responses for the highest drying temperature. The lowest payback period (Pb) of the drying system was estimated to be 0.45 years for T = 70 °C and Q = 0.28 m3 h−1. The optimum drying time, Deff, total energy consumption (Et), SEC, η, and total color change (ΔE) were estimated to be 9 h, 3.3 × 10−7 m2 s−1, 33.91 kWh, 22.04 kWh.kg−1, 27.69%, and 1.69, respectively.

Electrocoagulation for the decolorization of textile wastewater in single-channel reactor: Response surface methodology for optimization and a novel model exploitation

This work focuses on the multi-objective optimization of the electrocoagulation process. The goal is to decolorize synthetic Reactive Black 5 wastewater using a single-channel reactor operating in a closed-circuit. Response Surface Methodology (RSM) is employed for the optimization. The Central Composite Design (CCD) is used to study the empirical relationship between two responses (decolorization efficiency (η) and energy consumption (WC)) and four independent variables (recirculation flow rate (Qv), current intensity (I), electrolysis time (τe) and active electrode area (Sa)), while the desirability function (D) is applied to determine optimal conditions. In addition, a novel approach for the exploitation of the RSM model is developed to set correlations between the limit of η (ηLim) and corresponding independent variables’ limit values (below of which η < ηLim). The developed correlations showed a good agreement between the graphical data and the estimated values (R2 > 0.99). The CCD model fits well the experimental data with a R2 of 0.94 and 0.95, for η and WC, respectively. The analysis of variance allowed to build quadratic models for both responses. For a maximal overall desirability D of 0.88, optimal conditions are Qv = 70 L h−1, I = 100 mA, τe = 34.6 min and Sa = 621 cm2. Under these conditions, η = 70.9% and WC = 4.6 Wh.m−3 were achieved. The sludge characterization using X-ray diffraction, Scanning Electron Microscopy, and Fourier Transform InfraRed reveals the formation of the amorphous Al(OH)3 without other precipitated substances and authenticates the successful sorption of RB5 onto the Aluminum hydroxide flocs.

An environmentally friendly multifunctional soy protein adhesive composed of rosin acid derivative inspired from organic-inorganic hybrid structure

Soy protein adhesives have the advantages of low price and renewability but are limited by terrible bonding strength and poor water resistance, which restricts the practical application. Herein, inspired by the structure of mussels and organic-inorganic hybrid strengthening mechanism, a novel and bio-based soy protein adhesive (SPI-RA-CSA) based on rosin acid derivative and calcium sulfoaluminate was generated. According to the optimization of response surface methodology (RSM), the best proportion of adhesive SPI-RA6-CSA1 was chosen. The dry and wet shear strengths of SPI-RA6-CSA1 adhesive reached 1.92 and 1.35 MPa, respectively, which were 120% and 210% higher than original soy protein adhesive. Meanwhile, SPI-RA6-CSA1 adhesive showed excellent coating performance even if it was coated on wet wood veneer. In addition, SPI-RA6-CSA1 adhesive effectively inhibited the formation of mold and exhibited outstanding antimicrobial ability for Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli) with antimicrobial rates of 87.36% and 85.73%, respectively. What's more, SPI-RA6-CSA1 adhesive demonstrated prominent flame retardant properties reducing the harm of fire accidents. SPI-RA6-CSA1 adhesive also possessed the characteristics of low energy consumption and environmentally friendliness by Life Cycle Assessment (LCA). Therefore, this simple strategy provides workable guidance for the production of multifunctional and green soy protein adhesive.

Bacillus cereus-mediated biofermentation of Sardine offal waste: A novel approach to enhance nutritional value by Response Surface Methodology optimization

The rising protein demand in the aquaculture sector has significantly impacted fishmeal supply and pricing. Excessive use of fishmeal can lead to environmental issues and negatively impact marine biodiversity and human food security. Consequently, finding alternative fishmeal in aquaculture is crucial for economic and environmental sustainability. The present study aimed to determine how Bacillus cereus (MT355408) could enhance nutritional value of Sardine fish waste, which could replace fish meal in the market. Solid-state fermentation (SSF) represents a biotechnological method that utilizes microbes to convert discarded fish byproducts into valuable products. The bacterial ability to produce enzymes was studied and optimised for its maximum production to be used as an inoculum for the SSF technique. Different prebiotic sources were also studied for better upliftment of bacteria in the solid-state surface. A single-factor analysis was conducted to investigate the influence of varying prebiotic concentrations, inoculum quantity, and fermentation duration on protein breakdown. After studying the single-factor tests, a further response surface model was employed for better yield. The results indicated that the highest protein yield could be achieved with a fermentation time of 132.893 hours, a prebiotic quantity of 25%, and an inoculum quantity of 5.3%. The study's findings also affirmed that the model was vital in enhancing the crude protein content during fermentation. In conclusion, the model's results contribute valuable insights into fermentation processes, offering practical implications for enhancing protein content and digestibility in similar contexts.

Visible LED active photocatalyst based on cerium doped titania for Rhodamine B degradation: Radical's contribution, stability and response surface methodology optimization

In this study, cerium modified TiO2 catalyst was prepared and the performance on the photocatalytic degradation of Rhodamine B (RhB) in aqueous solution was evaluated. For the band gap engineering, three different percentages of Ce-doped TiO2 (3, 5 and 7 % mol) were synthesized using the simple, efficient, and low-cost sol-gel method. Structural, morphological, and optical analysis, performed by XRD-Rietveld, HRTEM, EDX, XPS and UV–Vis DRS techniques, indicate the formation of nanostructured Ce-doped TiO2 system with anatase phase and band gap in the visible range. Then, Ce-doping percentage, dose of photocatalyst and initial concentration of RhB effects on the removal efficiency were evaluated. The high removal efficiency of 70 % within 150 min was recorded under the condition of: 7 % Ce-doped TiO2 sample, 1.33 g L−1 of dose, and a concentration of RhB of 5 mg L−1. Additionally, a response surface methodology (RSM) was developed for stablish optimal degradation parameters when varying relevant factors for water treatment of real effluents: pH, NaCl, and Cu concentrations. The resulting model shows that the pH is the variable with the highest effect on the photodegradation. Finally, a scavenger study revealed that OH• radicals play a key role in the reaction; and, albeit TOC analysis reveals only 8.55 % of mineralization yield. Cycling study shows that the photocatalyst kept active after 3 cycles of reutilization. The present paper further examinates the parameters affecting Visible LED driven degradation of organic dye in water using Ce-doped TiO2 system and represents an advance in the comprehension of dye degradation with low use of energy.

Model-Assisted Optimization of Cobalt Biosorption on Macroalgae Padina pavonica for Wastewater Treatment

The release of heavy metals into the environment as a result of industrial and agricultural activities represents one of the century’s most significant issues. Cobalt is a hazardous metal that is employed in a variety of industries. In this study, response surface methodology (RSM) combined with Box–Behnken design (BBD) was utilized to optimize the Co(II) ion removal from synthetic wastewater by the brown macroalga Padina pavonica. The influence of three factors, namely algal inoculum size, pH, and initial metal concentration, was assessed in optimization studies. RSM proposed a second-order quadratic model with a p-value of &lt;0.0001 and R2 of 0.984 for P. pavonica. According to the data related to RSM optimization, the maximum percentage of Co(II) removal of 84.3% was attained under the conditions of algal inoculum size of 5.98 g/L, pH of 6.73, and initial Co(II) concentration of 21.63 mg/L. The experimental data from the biosorption process were fitted well with the Langmuir, Freundlich, and Temkin isotherm models. The maximal Co(II) adsorption capacity was estimated using the Langmuir model to be 17.98 mg/g. Furthermore, the pseudo-second-order kinetic model was shown to have the best fit for Co biosorption by P. pavonica, showing that the mechanism of Co(II) biosorption was chemisorption controlled by surface biosorption and intra-particle diffusion. Thermodynamic parameters were also investigated to evaluate the Gibbs free energy for the Co(II) ion, which was positive, showing that the biosorption process is nonspontaneous and exothermic, and the cobalt biosorption rate decreases with increasing temperature. Algal biomass was characterized by Fourier transform infrared spectroscopy, scanning electron microscopy, and energy dispersive spectroscopy. These analyses revealed the biosorbent’s diverse functional groups and porous, rough appearance. Therefore, P. pavonica can be used to implement sustainable, eco-friendly, and acceptable solutions to water pollution problems.

Optimization and mechanism of the novel eco-friendly additives for solidification and stabilization of dredged sediment.

Solidification/stabilization technology is commonly used in the rehabilitation of dredged sediment due to its cost-effectiveness. However, traditional solidification/stabilization technology relies on cement, which increases the risk of soil alkalization and leads to increased CO2 emissions during cement production. To address this issue, this study proposed an innovative approach by incorporating bentonite and citrus peel powder as additives in the solidifying agent, with the aim of reducing cement usage in the dredged sediment solidification process. The research results showed that there is a significant interaction among cement, bentonite, and citrus peel powder. After response surface methodology (RSM) optimization, the optimal ratio of the cementitious mixture was determined to be 14.86 g/kg for cement, 5.85 g/kg for bentonite, and 9.31 g/kg for citrus peel powder. The unconfined compressive strength (UCS) of the solidified sediments reached 3144.84 kPa. The reaction products of the solidification materials, when mixed with sediment, facilitated adsorption, gelation, and network structure connection. Simultaneously, the leaching concentration of heavy metals was significantly decreased with five heavy metals (Zn, As, Cd, Hg, and Pb) leaching concentrations decreasing by more than 50%, which met the prescribed thresholds for green planting. This study demonstrated the ecological benefits of employing bentonite and citrus peel powder in the solidification process of dredged sediment, providing an effective solution for sediment solidification.

Microencapsulation of Acalypha indica Linn. Extracts Using Chitosan-Polycaprolactone Blends

Polymer encapsulation is commonly adopted in drug delivery systems to form encapsulation that can assist in delivering active compounds to the targeted area. Acalypha indica (AI) crude extract was obtained from AI plants through ultrasound-assisted extraction. It is naturally unstable in the external environment and, thus, needs to be encapsulated to protect against volatility. Herein, this study emphasized the development of the encapsulations of AI extracts using a chitosan-polycaprolactone (PCL) blend by emulsion-solvent evaporation and freeze-dried methods. Four parameters for Al encapsulation were studied by fixing one parameter at a time. The percentage of encapsulation efficiency (EE%) was recorded as a response for each parameter. The study proceeded with central composite design (CCD) as the response surface methodology (RSM) optimization tool to study the interactions between the factors. Central points were taken from the preliminary data obtained in one-parameter experiments. The validation was carried out with two data of the highest and lowest EE% suggested by CCD. The highest EE% recorded was 98.70%, and the lowest EE% was 87.80%. The results showed a difference between predicted and experimental values at a percentage lower than 7.5%. Fourier Transform Infrared Spectroscopy (FTIR), scanning electron microscopy (SEM), particle size analyzer, and zeta potential were used to analyze the properties of selected microencapsulated samples. Overall, the encapsulation of AI extracts was successful and has the potential to be used in drug delivery.